292 lines
13 KiB
Python
292 lines
13 KiB
Python
import os
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import math
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import argparse
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import random
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import logging
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from tqdm import tqdm
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import torch
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from data.data_sampler import DistIterSampler
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from utils import util, options as option
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from data import create_dataloader, create_dataset
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from models.ExtensibleTrainer import ExtensibleTrainer
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from time import time
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class Trainer:
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def init_dist(self, backend, **kwargs):
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# These packages have globals that screw with Windows, so only import them if needed.
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import torch.distributed as dist
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import torch.multiprocessing as mp
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"""initialization for distributed training"""
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if mp.get_start_method(allow_none=True) != 'spawn':
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mp.set_start_method('spawn')
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self.rank = int(os.environ['RANK'])
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num_gpus = torch.cuda.device_count()
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torch.cuda.set_device(self.rank % num_gpus)
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dist.init_process_group(backend=backend, **kwargs)
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def init(self, opt, launcher, all_networks={}):
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self._profile = False
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self.val_compute_psnr = opt['eval']['compute_psnr'] if 'compute_psnr' in opt['eval'] else True
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self.val_compute_fea = opt['eval']['compute_fea'] if 'compute_fea' in opt['eval'] else True
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#### distributed training settings
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if len(opt['gpu_ids']) == 1 and torch.cuda.device_count() > 1:
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gpu = input(
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'I noticed you have multiple GPUs. Starting two jobs on the same GPU sucks. Please confirm which GPU'
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'you want to use. Press enter to use the specified one [%s]' % (opt['gpu_ids']))
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if gpu:
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opt['gpu_ids'] = [int(gpu)]
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if launcher == 'none': # disabled distributed training
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opt['dist'] = False
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self.rank = -1
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print('Disabled distributed training.')
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else:
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opt['dist'] = True
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self.init_dist()
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world_size = torch.distributed.get_world_size()
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self.rank = torch.distributed.get_rank()
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#### loading resume state if exists
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if opt['path'].get('resume_state', None):
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# distributed resuming: all load into default GPU
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device_id = torch.cuda.current_device()
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resume_state = torch.load(opt['path']['resume_state'],
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map_location=lambda storage, loc: storage.cuda(device_id))
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option.check_resume(opt, resume_state['iter']) # check resume options
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else:
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resume_state = None
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#### mkdir and loggers
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if self.rank <= 0: # normal training (self.rank -1) OR distributed training (self.rank 0)
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if resume_state is None:
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util.mkdir_and_rename(
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opt['path']['experiments_root']) # rename experiment folder if exists
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util.mkdirs(
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(path for key, path in opt['path'].items() if not key == 'experiments_root' and path is not None
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and 'pretrain_model' not in key and 'resume' not in key))
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# config loggers. Before it, the log will not work
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util.setup_logger('base', opt['path']['log'], 'train_' + opt['name'], level=logging.INFO,
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screen=True, tofile=True)
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self.logger = logging.getLogger('base')
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self.logger.info(option.dict2str(opt))
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# tensorboard logger
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if opt['use_tb_logger'] and 'debug' not in opt['name']:
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self.tb_logger_path = os.path.join(opt['path']['experiments_root'], 'tb_logger')
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version = float(torch.__version__[0:3])
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if version >= 1.1: # PyTorch 1.1
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from torch.utils.tensorboard import SummaryWriter
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else:
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self.self.logger.info(
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'You are using PyTorch {}. Tensorboard will use [tensorboardX]'.format(version))
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from tensorboardX import SummaryWriter
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self.tb_logger = SummaryWriter(log_dir=self.tb_logger_path)
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else:
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util.setup_logger('base', opt['path']['log'], 'train', level=logging.INFO, screen=True)
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self.logger = logging.getLogger('base')
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# convert to NoneDict, which returns None for missing keys
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opt = option.dict_to_nonedict(opt)
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self.opt = opt
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#### random seed
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seed = opt['train']['manual_seed']
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if seed is None:
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seed = random.randint(1, 10000)
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if self.rank <= 0:
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self.logger.info('Random seed: {}'.format(seed))
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util.set_random_seed(seed)
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torch.backends.cudnn.benchmark = True
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# torch.backends.cudnn.deterministic = True
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# torch.autograd.set_detect_anomaly(True)
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# Save the compiled opt dict to the global loaded_options variable.
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util.loaded_options = opt
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#### create train and val dataloader
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dataset_ratio = 1 # enlarge the size of each epoch
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for phase, dataset_opt in opt['datasets'].items():
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if phase == 'train':
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self.train_set = create_dataset(dataset_opt)
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train_size = int(math.ceil(len(self.train_set) / dataset_opt['batch_size']))
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total_iters = int(opt['train']['niter'])
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self.total_epochs = int(math.ceil(total_iters / train_size))
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if opt['dist']:
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train_sampler = DistIterSampler(self.train_set, world_size, self.rank, dataset_ratio)
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self.total_epochs = int(math.ceil(total_iters / (train_size * dataset_ratio)))
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else:
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train_sampler = None
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self.train_loader = create_dataloader(self.train_set, dataset_opt, opt, train_sampler)
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if self.rank <= 0:
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self.logger.info('Number of train images: {:,d}, iters: {:,d}'.format(
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len(self.train_set), train_size))
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self.logger.info('Total epochs needed: {:d} for iters {:,d}'.format(
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self.total_epochs, total_iters))
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elif phase == 'val':
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self.val_set = create_dataset(dataset_opt)
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self.val_loader = create_dataloader(self.val_set, dataset_opt, opt, None)
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if self.rank <= 0:
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self.logger.info('Number of val images in [{:s}]: {:d}'.format(
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dataset_opt['name'], len(self.val_set)))
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else:
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raise NotImplementedError('Phase [{:s}] is not recognized.'.format(phase))
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assert self.train_loader is not None
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#### create model
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self.model = ExtensibleTrainer(opt, cached_networks=all_networks)
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#### resume training
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if resume_state:
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self.logger.info('Resuming training from epoch: {}, iter: {}.'.format(
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resume_state['epoch'], resume_state['iter']))
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self.start_epoch = resume_state['epoch']
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self.current_step = resume_state['iter']
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self.model.resume_training(resume_state, 'amp_opt_level' in opt.keys()) # handle optimizers and schedulers
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else:
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self.current_step = -1 if 'start_step' not in opt.keys() else opt['start_step']
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self.start_epoch = 0
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if 'force_start_step' in opt.keys():
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self.current_step = opt['force_start_step']
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def do_step(self, train_data):
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if self._profile:
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print("Data fetch: %f" % (time() - _t))
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_t = time()
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opt = self.opt
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self.current_step += 1
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#### update learning rate
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self.model.update_learning_rate(self.current_step, warmup_iter=opt['train']['warmup_iter'])
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#### training
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if self._profile:
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print("Update LR: %f" % (time() - _t))
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_t = time()
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self.model.feed_data(train_data)
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self.model.optimize_parameters(self.current_step)
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if self._profile:
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print("Model feed + step: %f" % (time() - _t))
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_t = time()
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#### log
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if self.current_step % opt['logger']['print_freq'] == 0 and self.rank <= 0:
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logs = self.model.get_current_log(self.current_step)
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message = '[epoch:{:3d}, iter:{:8,d}, lr:('.format(self.epoch, self.current_step)
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for v in self.model.get_current_learning_rate():
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message += '{:.3e},'.format(v)
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message += ')] '
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for k, v in logs.items():
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if 'histogram' in k:
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self.tb_logger.add_histogram(k, v, self.current_step)
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elif isinstance(v, dict):
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self.tb_logger.add_scalars(k, v, self.current_step)
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else:
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message += '{:s}: {:.4e} '.format(k, v)
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# tensorboard logger
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if opt['use_tb_logger'] and 'debug' not in opt['name']:
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self.tb_logger.add_scalar(k, v, self.current_step)
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self.logger.info(message)
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#### save models and training states
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if self.current_step % opt['logger']['save_checkpoint_freq'] == 0:
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if self.rank <= 0:
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self.logger.info('Saving models and training states.')
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self.model.save(self.current_step)
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self.model.save_training_state(self.epoch, self.current_step)
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if 'alt_path' in opt['path'].keys():
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import shutil
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print("Synchronizing tb_logger to alt_path..")
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alt_tblogger = os.path.join(opt['path']['alt_path'], "tb_logger")
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shutil.rmtree(alt_tblogger, ignore_errors=True)
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shutil.copytree(self.tb_logger_path, alt_tblogger)
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#### validation
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if opt['datasets'].get('val', None) and self.current_step % opt['train']['val_freq'] == 0:
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if opt['model'] in ['sr', 'srgan', 'corruptgan', 'spsrgan',
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'extensibletrainer'] and self.rank <= 0: # image restoration validation
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avg_psnr = 0.
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avg_fea_loss = 0.
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idx = 0
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val_tqdm = tqdm(self.val_loader)
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for val_data in val_tqdm:
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idx += 1
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for b in range(len(val_data['GT_path'])):
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img_name = os.path.splitext(os.path.basename(val_data['GT_path'][b]))[0]
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img_dir = os.path.join(opt['path']['val_images'], img_name)
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util.mkdir(img_dir)
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self.model.feed_data(val_data)
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self.model.test()
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visuals = self.model.get_current_visuals()
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if visuals is None:
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continue
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sr_img = util.tensor2img(visuals['rlt'][b]) # uint8
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# calculate PSNR
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if self.val_compute_psnr:
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gt_img = util.tensor2img(visuals['GT'][b]) # uint8
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sr_img, gt_img = util.crop_border([sr_img, gt_img], opt['scale'])
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avg_psnr += util.calculate_psnr(sr_img, gt_img)
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# calculate fea loss
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if self.val_compute_fea:
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avg_fea_loss += self.model.compute_fea_loss(visuals['rlt'][b], visuals['GT'][b])
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# Save SR images for reference
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img_base_name = '{:s}_{:d}.png'.format(img_name, self.current_step)
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save_img_path = os.path.join(img_dir, img_base_name)
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util.save_img(sr_img, save_img_path)
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avg_psnr = avg_psnr / idx
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avg_fea_loss = avg_fea_loss / idx
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# log
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self.logger.info('# Validation # PSNR: {:.4e} Fea: {:.4e}'.format(avg_psnr, avg_fea_loss))
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# tensorboard logger
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if opt['use_tb_logger'] and 'debug' not in opt['name'] and self.rank <= 0:
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self.tb_logger.add_scalar('val_psnr', avg_psnr, self.current_step)
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self.tb_logger.add_scalar('val_fea', avg_fea_loss, self.current_step)
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def do_training(self):
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self.logger.info('Start training from epoch: {:d}, iter: {:d}'.format(self.start_epoch, self.current_step))
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for epoch in range(self.start_epoch, self.total_epochs + 1):
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self.epoch = epoch
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if opt['dist']:
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self.train_sampler.set_epoch(epoch)
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tq_ldr = tqdm(self.train_loader)
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_t = time()
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for train_data in tq_ldr:
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self.do_step(train_data)
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def create_training_generator(self, index):
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self.logger.info('Start training from epoch: {:d}, iter: {:d}'.format(self.start_epoch, self.current_step))
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for epoch in range(self.start_epoch, self.total_epochs + 1):
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self.epoch = epoch
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if self.opt['dist']:
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self.train_sampler.set_epoch(epoch)
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tq_ldr = tqdm(self.train_loader, position=index)
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_t = time()
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for train_data in tq_ldr:
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yield self.model
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self.do_step(train_data)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_prog_imgset_multifaceted_chained.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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args = parser.parse_args()
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opt = option.parse(args.opt, is_train=True)
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trainer = Trainer()
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trainer.init(opt, args.launcher)
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trainer.do_training()
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